Neglected Genetic Coefficients for Bacterial Diversity as a Supporting Tool for Public Health and Wastewater-Based Epidemiology

Abstract

In the review, the collection of population genetics papers from 1973 to 2025 comprises 400 publications, 81 of which were significant and consulted with representatives from water and sewage companies. Reviewed Proteobacteria (mean H_S = 0.42), Firmicutes (mean H_S = 0.43), Actinobacteria (mean H_S = 0.33), and Spirochaetes (mean H_S = 0.54) represent the 60 species under investigation through the lens of “h” coefficients related to gene diversity and expected heterozygosity. The research also included ESKAPE, emerging pathogens, bacterial indicators of wastewater treatment efficiency, environmental sanitary surveillance and public health. The restoration of the expected heterozygosity for haploids “h” was proposed in wastewater-based epidemiology as an innovative tool for public health. The unique “h” coefficient allows for the comparison of genetic variability in various organisms, regardless of their ploidy, using multiple markers and traits. The parameter represents a noble character for both the variability of phenotypes (proteins) and genotypes (nucleic acids). Leveraging the genetic diversity highlighted by the “h” coefficient can support wastewater-based epidemiology, offering the ability to predict the stages and trajectories of disease outbreaks.

1. Genetic Diversity Coefficients in Bacteria as a Support for WBE Action

The data on the genetic structure of bacterial populations, as described by population genetics methods, are often fragmented. This lack of consolidation is primarily due to the historical focus on a one-dimensional medical approach to epidemiological research. Initially, this approach aimed to identify species-specific markers quickly, especially for pathogenic bacterial groups [1,2]. Consequently, most analyses have concentrated on selected and characteristic polymorphisms of sequences. Moreover, the search for universal species-specific markers does not necessitate the application of population genetics parameters on the bacteria being examined. The application of population genetic methods has been limited, particularly concerning the genetic variability of archaebacteria. In the case of eubacteria, gene diversity profiles have been obtained for about 40 species from over 100 well-characterized bacterial genomes [3,4]. Most data related to enzymatic loci testing reveal the protein-level diversity among bacteria. Over the past four decades, many new parameters of genetic diversity based on DNA markers have been recognized, and new sequencing approaches have been developed [5,6,7]. The significance of these molecular tools extends to preventing disease, prolonging life, promoting public physical and mental health, improving personal and environmental hygiene, controlling disease factors, and organizing healthcare. Additionally, the last three decades have underscored the importance of bacterial genetics, bioinformatics, and molecular epidemiology in supporting public health [8,9]. Furthermore, open data sources, such as those from used water and wastewater treatment, have become crucial for monitoring biological factors, particularly in the field of wastewater-based epidemiology (WBE) [10].

Molecular epidemiology encompasses a range of activities, techniques, and methodological approaches aimed at gathering materials and information about the occurrence and behavior of biological factors, such as bacteria [11]. This information is crucial for developing countermeasures and procedures when an epidemic is suspected. Subsequently, epidemiological monitoring is conducted regularly, employing standardized methods for data collection, storage, and processing [12]. Various environmental elements are monitored, including air, groundwater, surface water, seawater, coastal water, transitional water, wastewater, and sludge [13,14]. Sewage contains over 21 families of bacteria that may be pathogenic to human health. Additionally, Wu et al. [15] identified one billion microbial phylotypes, including 28 operational taxonomic units (OTUs), in the microbiome of activated sludge systems. Understanding the population structure of specific epidemiological factors can enhance the flow of information from collection points to databases and ultimately to physicians or epidemiologists, especially when mitigation actions are necessary. The increasing presence of multi-, extended-, and pan-drug-resistant strains of Escherichia coli, Salmonella/Shigella, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and other enteric bacteria (collectively referred to as ESKAPE) is becoming a growing concern [16,17]. At the same time, Directive (EU) 2024/3019 emphasizes the importance of assessing emerging diseases, particularly their surveillance algorithms. The continuous emergence of new traits in environmental bacteria, their expanding host range, and levels of pathogenicity are partially driven by anthropogenic pressures, including the increasing volume of solid waste, sewage, and pharmaceuticals in the environment [18,19].

There are many markers of interest for whole bacterial community evaluation (WBE), particularly those related to proteins and genes that promote resistance to chemotherapy [20]. These markers, which can include mobile elements that are either dispersed or clustered and may be organized as pathogenic islands or multitasking operons [21], flow into wastewater treatment plants from municipal or agricultural sources. After undergoing treatment—ideally through activated sludge processes—they are released directly into water bodies. There is still limited knowledge regarding the potential presence of ESKAPE organisms in wastewater, including their genetic sequences and fragmented material, which may pose environmental risks [22]. Furthermore, there is an urgent need to gather data on the quality and quantity of Legionella species (including L. pneumophila) present in effluent from wastewater treatment plants, especially in light of legislative requirements that promote the reuse of treated wastewater [23,24].

To recognize genetic changes in bacterial populations, population genetics parameters are effectively utilized [25,26,27]. Estimating genetic variability at the species or population level is based on allele frequency (P), the percentage of polymorphic loci (A), and the degree of expected heterozygosity (H_T or H_S) [28,29]. The coefficient of expected heterozygosity, calculated from allele frequency—especially for haploids—allows for the comparison of variation levels among evolutionarily distant organisms, even if they are at different ploidy stages. Because haploid organisms lack a diploid stage, the H_T or H_S coefficients are defined as gene diversity [30,31,32]. The H_T coefficient reflects the genetic diversity of a species based on the existing polymorphism in the analyzed loci. H_T values depend on the number and frequency of alleles present at these loci. The greater the number of loci with lower allele frequencies in a given species, the higher the H_T value. In haploid populations, the H_S parameter indicates the proportion of loci that differ between two randomly selected individuals. This means it can suggest the likelihood that the individuals differ at least at one locus. By utilizing H_T and H_S parameters, it is possible to calculate both the proportion of population genetic variability relative to species variability (D_st) and the inter-population variability in relation to the overall variation within the species (G_st) [30,33,34].

Epidemiology encompasses a set of methodological approaches and concepts designed to track the causes of health disorders, drawing heavily on mathematical principles [35]. The primary objective of preventive epidemic actions is to identify factors contributing to health issues, understand their nature and extent, determine how they affect different biotypes, and assess whether they are in an invasive or eradication phase [36,37]. Periodic and consequence epidemiological monitoring involves evaluating and predicting the state of the environment, as well as collecting, processing, and disseminating information about natural elements of environment. One of the most crucial natural elements is clean water, along with the wastewater produced as a byproduct of human activity. Wastewater is a complex mixture of particulate matter of varying sizes, chemical compounds, and biological factors [38]. The focus of wastewater-based epidemiology (WBE) is on wastewater generated from diverse sources, such as agricultural runoff and domestic sewage from urbanized areas. This area of study includes various biological genres, including bacteria (43), microscopic fungi (29), parasites (15), viruses (13), ciliates (7), and protists (3) that are significant to public health [39,40,41,42,43]. Among these are human pathogens and organisms that are excreted in feces and spread through sewage. These biological agents form a genetically diverse group with varying ploidy levels. Estimating key parameters for WBE, such as the levels of intra- and inter-species variability among these agents, can be valuable for monitoring wastewater treatment processes [44]. Values of heterozygosity parameters may aid in assessing different types of sedimentation parameters and saprobic indices, as well as species and genus balance. Additionally, the genetic parameters of He can help evaluate risks associated with biological agents, genome fragments, or their expression products in the environment. For instance, parameters like genetic variability and diversity (N_e, H_e, G_st, D_st, I, and D) can enhance database algorithms related to the effectiveness of surfactants, disinfectants, and personal protective equipment [33,45]. Routinely used parameters such as N_e, H_e, G_st, D_st, I, and D serve to assess genetic variability in various organisms, including plants, animals, protozoa, and microscopic fungi. Values of these parameters help track species distributions and the acquisition and loss of phenotypic traits over time. This information is crucial for implementing appropriate epidemiological and clinical measures [46]. Therefore, a comprehensive approach to the genetic variability of biological factors in wastewater is essential, focusing not only on genetic elements but also, and perhaps primarily, on phenotypic traits. Gene products resulting from the interaction of microorganisms’ genetic information with their environment remain pivotal [47]. Currently, WBE does not fully utilize the established routes of population genetics for pre-epidemic and epidemic assessments, nor does it leverage the neglected parameters in the population genetics of biological factors present in wastewater.

2. Enzymatic Diversity About Epidemiological Interest for Public Health

Pathogenic bacteria possess a widely diverse population structure—from clonal to panmictic. Mycobacterium tuberculosis and Borrelia burgdorferi populations are characterized with a clonal structure in comparison to Neisseria gonorrhoeae, Bacillus subtilis and Rhizobium meliloti [48,49,50]. Studies of enzymatic loci in Haemophilus influenzae strains that are resistant to aminoglycosides, indicate a wide range of population structure from pure clonal to pseudo panmictic [51]. Furthermore, the time of being a clone in bacterial strains is not stable. In Haemophilus influenzae that time is shorter than in Escherichia coli clones [52,53]. Pseudomonas sp., similarly to Vibrio cholerae, possesses a strong clonal structure with mechanisms that maintain such a structure up to 28 months [54,55]. Efficient correction mechanisms may maintain the clonal structure for decades despite much more frequent recombination in V. cholerae than in E. coli and Salmonella enteritica [56,57].

The bacteria that are most well-known for their protein variability are classified within the phylum Proteobacteria. In particular, the γ-Proteobacteria class of this phylum, which includes all human pathogens, exhibits a wide range of variability, with values ranging from H_S = 0.03 to 0.89 and an average of 0.46 [58,59]. Differences in this genetic diversity parameter can be observed among families, species, and even within a single species, such as Escherichia coli. For example, the variability for different species of Pseudomonas ranges from 0.08 to 0.89, while Escherichia species show values between 0.27 and 0.52, Haemophilus sp. have values between 0.40 and 0.57, and Pasteurella sp. range from 0.29 to 0.36 (see

Table 1. Level of genetic diversity in Proteobacteria phylum * based on aggregated enzymatic data for many different enzymes (modified from [3,4]).

Class	Species/Strain	HS	Reference
Class I α-Proteobacteria	Acetobacter diazotrophicus	0.06	[60]
	Bradyrhizobium sp.	0.64	[61]
	Rhizobium etli biovar phaseoli	0.49	[62]
	Rhizobium leguminosarum biov. phaseoli	0.69	[63]
	Rhizobium leguminosarum biov. viciae	0.58	[64]
	Rhizobium legumonosarum biov. trifolii	0.35–0.43	[65]
Class II β-Proteobacteria	Burkholderia cepacia	0.52–0.57	[66]
	Bordetella sp.	0.28	[67]
	Neisseria gonorhoeae	0.23–0.49	[68,69]
	Neisseria meningitidis	0.55	[70]
Class III γ-Proteobacteria	Actinobacillus pleuropneumoniae	0.31	[71]
	Citrobacter diversus	0.24	[72]
	Escherichia coli	0.34–0.52	[73,74]
	E. coli (O101 strain)	0.27	[75]
	E. coli (O20 strain)	0.45	[75]
	E. coli (O9 strain)	0.39	[75]
	Edwardsiella ictaluri	0.03	[58]
	Haemophilus influenzae	0.47–0.57	[52,53]
	Haemophilus parasuis	0.40	[76]
	Klabsiella sp.	0.23	[77]
	Legionella pneumophila	0.31	[78]
	Pasteurella multocida	0.30–0.36	[79]
	Pasteurella trechalosi	0.29	[80]
	Pseudomonas sp.	0.89	[55]
	Pseudomonas syringae pathovar syringae	0.48	[81]
	Pseudomonas syringae pv. tomato	0.08	[81]
	Salmonella sp.	0.56–0.63	[82]
	Salmonella enteritica	0.63	[67]
	Vibrio anguillarum	0.13	[59]
	Vibrio cholerae	0.44	[83]
	Vibrio ordalii	0.11	[59]
Class V ε-Proteobacteria	Campylobacter jejuni	0.43	[50]
	Campylobacter sp.	0.63	[84]
	Helicobacter pylori	0.73	[67]
	Mean within all	0.42

Note: * Taxonomic partition is based on Tortora et al. [85].

and Figure 1).

The Spirochaetes phylum exhibits the highest level of genetic variability, with a heterozygosity (H_S) value of 0.54. This phylum includes several human pathogens known for their characteristic spiral cell shape. Within the second family of this phylum, the H_S parameter for Serpulina species varies significantly, ranging from 0.18 to 0.62. In contrast, bacteria from the Firmicutes phylum demonstrate a lower level of genetic variability. These bacteria have strong cell membranes, similar to those found in Gram-positive organisms. Variations in H_S values are observed among families, such as Aeromonas and Streptococcus, as well as among species within the Clostridium genus (

Table 2. Level of genetic diversity in Firmicutes, Actinobacteria and Spirochaetes phyla * based on aggregated enzymatic data for many different enzymes (modified from [3,4]).

Class	Species/Strain	HS	Reference
Class I Clostridia	Aeromonas salmonicida	0.037	[87]
	Aeromonas sp.	0.09–0.88	[88]
	Clostridium perfringens	0.34–0.81	[89]
Class II Mollicutes	Erysipelothrix sp.	0.31	[67]
Class III Bacilli	Bacillus subtilis	0.28	[90]
	Listeria monocytogenes	0.42	[91,92]
	Staphylococcus aureus	0.29	[67]
	Streptococcus pneumoniae	0.32–0.41	[86]
	Streptococcus pyogenes	0.42	[67]
	Streptococcus sp.	0.86	[93]
	Streptococcus suis	0.51	[51]
	Mean within Firmicutes	0.43
	Actinobacteria
Class I Actinobacteria	Renibacterium salmoninarum	0.16	[94]
	Dermatophilus congolensis	0.41	[95]
	Mycobacterium avium	0.29	[96]
	Mycobacterium avium complex	0.38	[97]
	Mycobacterium intracellulare	0.33–0.38	[98]
	Mycobacterium scrofulaceum	0.66
	Mycobacterium sp.	0.58	[67]
	Mycobacterium tuberculosis complex	0.1	[99]
	Mean within Actinobacteria	0.33
	Spirochaetes
Class I Spirochaetes	Borrelia burgdorferi	0.67	[48]
	Borrelia burgdorferi sensu lato	0.65–0.75	[100,101]
	Borrelia sp.	0.67	[67]
	Treponema sp.	0.75	[102]
	Serpulina hyodysenteriae	0.29	[103]
	Serpulina pilosicoli	0.18	[104]
	Serpulina sp.	0.32–0.62	[105,106]
	Mean within Spirochaetes	0.54

Note: * Taxonomic partition is based on Tortora et al. [85].

) [86]. The Actinobacteria phylum shows the lowest genetic diversity, as indicated by enzymatic traits (H_S = 0.33). These organisms often form specific fungal-like colonies. Among the Actinobacteria, the Mycobacteriaceae family displays notable variability, with a wide range of H_S values.

One of the most variable pathogens is Helicobacter pylori. The genetic diversity coefficient (H_S) obtained through isoenzyme electrophoresis was 0.74 (see Table 1). The high genetic variability of this pathogen complicates the detection of identical sequences in two unrelated strains. Frequent recombination and a lack of mechanisms to eliminate recombined genotypes result in an almost unlimited number of unique alleles [50,107]. Additionally, H. pylori strains exhibit a tendency to attach to their host, leading to the primary transmission of this bacterium within infected families (from mother to children) [50,67]. In contrast, the probability of selecting two genetically distinct strains in a given locus for Pseudomonas stutzeri, P. balearica, P. pseudoalcaligenes, and P. mendocina is highest among bacteria, with an average genetic diversity coefficient (H_S) of 0.88 [55]. Pseudomonas aeruginosa is an exception within this genus; despite inhabiting a wide range of environments—being either environmental or a pathogenic bacterium—the genetic diversity coefficient is relatively low, ranging from 0.14 to 0.23 [54]. For Mycobacterium intracellulare, randomly selected bacteria differ from one another at 38 out of 100 loci (H_S = 0.38), while Mycobacterium scrofulaceum shows differences at 66 out of 100 loci (H_S = 0.66) [98]. A similar level of genetic variability is found in pathogenic bacteria such as Haemophilus influenzae, where the probability of choosing two cells with different alleles at the same locus ranges from 0.55 to 0.59 (average H_S = 0.57) [52,53]. Randomly selected V. cholerae strains differ from one another in an average of 44% of examined enzymatic loci. Comparatively, other pathogens show variation as follows: Neisseria gonorrhoeae at 41%, Escherichia coli at 43%, Staphylococcus aureus at 29%, Legionella pneumophila at 31%, and Salmonella enteritica at 63% [78]. Within Pneumococcus, strains of different serotypes that share the same allele at every locus occur with an average frequency ranging from 3.5 to 1.6 × 10⁻⁵. Streptococcus pneumoniae exhibits a low clonal structure while displaying significant genetic diversity (H = 0.82) [86].

Recent origins, a high level of recombination in the population, and stages of expansion lead to fluctuations in genetic diversity parameters for strains of Campylobacter jejuni, Yersinia pestis, and Bacteroides fragilis, with an H_S of 0.40 (SD = 0.12) [50,108]. The invasive nature and widespread distribution of C. jejuni clones, particularly the best-adapted strains, are facilitated by human activities [50]. For Pasteurella multocida and Pasteurella trehalosi populations, random cells may vary from one another in about 30 out of 100 loci (H = 0.30) [76]. Pasteurella trehalosi primarily affects the lymphatic glands of sheep and demonstrates host specificity and tissue tropism, similar to Mycobacterium tuberculosis. It is also an opportunistic bacterium, typically infecting only individuals with weakened immune systems. The low level of genetic variability and lack of significant genotypic diversity in this bacterium can be attributed to the young age of the host, a low effective population number (N_e), and rare recombinations [80]. An even lower level of genetic variability is observed in the animal pathogen Serpulina hyodysenteriae, which has a coefficient of H_S = 0.28 [106]. The level of variability in E. coli, a commensal and pathogen for mammals and birds, depends on the ecological niche it occupies (see

Table 3. Level of genetic diversity (H_S) in E. coli strains in relation to origin based on enzymatic data (modified reprint from [3] with permission and based on [49,110].

Origin of Bacteria	Strain Number	HS
Human (average)	-	0.42
Human (Australia)	41	0.57
Human (Mexico)	131	0.71
Carnivora individuals	34	0.65
Artiodactyla individuals	11	0.51
Marsupialia individuals	28	0.60
Rodents (Australia)	17	0.52
Rodents (Mexico)	34	0.64
ECOR collection standard *	13	0.49
Sewage	-	0.61

Note: * ECOR—E. coli standard reference collection.

) [109].

3. DNA Sequence Diversity as a Valuable Indicator for Public Health and Environmental Hygiene

In addition to protein variability, bacteria exhibit a significant level of variability in their DNA sequences, which includes polymorphisms within species and among different strains. Genetic variability in the tubercle bacillus can be observed through polymorphisms of direct repeats (DR) in repeated DNA sequences [111,112]. Highly polymorphic interval sequences (DVR) vary in number and structure due to deletions and insertions, allowing for the evolution of Mycobacterium tuberculosis strains [113,114]. The polymorphism of DVR in the tubercle bacillus develops more slowly than changes in the insertion sequence IS6110, and it resembles the polymorphism observed in Streptococcus pyogenes [113]. This characteristic is useful for constructing ancestral sequences that have evolved through multiple deletions and insertions, leading to the emergence of recent strains. Furthermore, the DR regions enable the calibration of a molecular clock, which allows researchers to estimate the approximate time of evolution for specific strains [114].

Sreevatsan et al. [115] analyzed the structural gene polymorphism in Mycobacterium tuberculosis and demonstrated that this pathogen has less genetic variability compared to Shigella sonnei, suggesting it is a highly specialized pathogenic clone of Escherichia coli from a genetic perspective. The evolutionary history of some species remains unclear. For instance, based on differences in the katG and gyrA genes at both the single-nucleotide polymorphism (SNP) level and large sequence polymorphism (LSP), the Mycobacterium tuberculosis complex (MTB) is divided into three groups. There is ongoing debate among scientists regarding the ancestral strain of M. tuberculosis and a lack of consensus on its evolutionary strategy, rendering evolutionary considerations about MTB speculative. Restriction analyses of IS6110, DR, and PGRS have only suggested hypothetical phylogenetics for this complex. It remains uncertain whether the evolution of the M. tuberculosis complex is influenced by gene deletions in M. tuberculosis or by the acquisition (insertions) of DNA fragments from Mycobacterium bovis. In contrast, organisms like Caulobacter crescentus and Rickettsia prowazekii exhibit an evolutionary strategy characterized by significant genome size reduction through the loss of many genes [116]. This genome “simplification” is also observed in Campylobacter jejuni, which utilizes its flagella genes, flaA and flaB, as virulence factors and possesses a larger number of regulatory systems compared to, for example, Helicobacter pylori [117]. What is more, another the O157:H7 strain of E. coli has lost approximately 567 genes compared to the K-12 strain [118,119]. The evolution of Clostridium acetobutylicum is characterized by gene acquisition through horizontal gene transfer [120]. Recent findings suggest the presence of bacteria in bison 17,000 years ago that are more similar to Mycobacterium tuberculosis and Mycobacterium africanum than to Mycobacterium bovis. This evidence could support the hypothesis that the evolution of the Mycobacterium tuberculosis complex is influenced by gene transfer from the M. tuberculosis genome to the M. bovis genome. These findings challenge the prevailing theories that suggest tuberculosis originated from domestic cattle.

There is relatively little information available regarding the level of genetic diversity in bacteria as it relates to epidemiological studies. Some research has been conducted to understand evolutionary types, sources of virulence, and mechanisms of resistance. However, we currently lack sufficient data to compare the genetic diversity levels of pathogenic bacteria with those of environmental bacteria for these purposes. For instance, estimates of genetic diversity coefficients, H_S and H_T, indicate that Mycobacterium tuberculosis is more diverse than previously thought, with H_S being 0.29 [28]. This finding suggests that M. tuberculosis may have a stronger epidemiological potential and a greater impact on public health than we initially expected. It is possible that a similar relationship exists, whereby higher genetic diversity (or genome plasticity) correlates with a more significant public health impact for many other bacterial species. Similarly to the case of the tubercle bacillus, the perceived image of high or low gene diversity could be revised through methods such as NGS. Subsequently, fast markers could be evaluated de novo and applied for WBE purposes (see

Table 4. Selected markers for WBE, water treatment technology, wastewater treatment and environmental, sanitary and hygienic surveillance other than commonly used in diagnostic tests [4].

Markers	Selected Representatives	Samples of Genes or Results of Their Expression
Quorum sensing inducers	Vibrio fischeri, Pseudomonas aeruginosa, Erwinia stewartia, Aeromonas hydrophila, Staphylococcus aureus, Streptococcus pneumoniae, Bacillus subtilis, Enterococcus faecalis	HHL *, OHHL *, HBHL *, 7,8-cis-HtDHL *, 3-OH PAME **, DSF ***, signal components A, plasmid transferring signals, competention and sporulation compounds, resistance mechanisms shifting, luminescening proteins and extracellular pigments or dyes
Glucose metabolism	Aeromonas hydrophila, Brucella suis, Staphylococcus aureus, Neisseria meningitidis, Yersinia pestis, Burkholderia pseudomallei, Bacillus anthracis, Halobacterium salinarum, Lactobacillus delbrueckii bulgaricus, Lactobacillus salivarius, Helicobacter pylori, Bacillus cereus	glk, glcK, nagC, Rru_A2486, HPAG1_1041, BC_0441, BC_4260
Dehydrogenase of CoA modulators	Escherichia coli, Haemophilus influenzae, Salmonella enterica serov. Cholerasuis, Salmonella typhimurium, Yersinia pseudotuberculosis, Aeromonas hydrophila, Nitrosomonas europaea, Pseudomonas aeruginosa, Mycobacterium leprae, Mycobacterium tuberculosis, Nitrosospira multiformis, Pseudomonas fluorescens, Mesorhizobium loti	aceEF, lpdA, pdhA, NOC_1254-1256, Nmul_A0358-59, Pfl_0462-63, Mlr_9539, AHA_3863
Monooxygenase pathways hydroxylating aromatic rings (phenol and toluen)	Pseudomonas putida, Pseudomonas putida, Comamonas. testosteroni, Acinetobacter sp., Burkholderia cepacian, Ralstonia pickettii, Pseudomonas stutzeri, Xanthobacter sp., Ralstonia eutropha	phlABCDEF, dmpKLMNOP, phhKLMNOP, ophKLMNOP, dsoABCDEF, tomA0A1A2A3A4A5, tbmABCDEF, tbuA1UBVA2C, touABCDEF, aamABCDEF, phlKLMOP
Vector sequences	Escherichia coli, Pseudomonas sp., Pseudomonas putida, Agrobacterium tumefaciens	pUC8, ColEI, RP4, F, TOL, pTiAch5
Selected bioreceptors	Pseudomonas putida, Pseudomonas fluorescens, Pseudomonas putida, Ralstonia eutropha, Pseudomonas fluorescens, Pseudomonas syringae	luxCDABE, nar, hang, cnrYXH, pUCD607, gfp
Target sequences for genetic manipulation	Lactic acid pathway bacteria among others Lactococcus lactis, Lactobacillus plantarum, L. fermentum, L. helveticus, L. delbrüeckii, L. bulgaricus and Streptococcus mutans	idh, aldB, ribC, glk, pepN, pepX, pepC, pepI, gdh, alaD, alr, phyC, amyA

Note: * acyl-homoserine lactones (AHLs) * was added; ** Methyl 3-hydroxypalmitate for diffusible signal mediating Quorum Sensing; *** cis-2-unsaturated fatty acids containing fatty acid carbon chains.

).

Finally, it is important to highlight the advancements in next-generation sequencing (NGS), which have significantly reduced the time and labor intensity associated with traditional genome screening methods. With a reaction capacity of approximately 5 Gbp per 24 h, NGS can capture the E. coli genome in just 3 min, or screen the genomes of 20 different bacterial species within 5 min using compact devices like the Oxford Nanopore Technologies (ONT, Oxford, UK) MinION. Equipment such as the GridION x5 or PromethION Oxford Nanopore Technologies (ONT, Oxford, UK) enables researchers to analyze differences among 48 small genomes or check the E. coli genome 48 times in just 3 min. Additionally, there is a robust logical framework for generating well-oriented data and for the decentralized exchange of data during the planning of epidemiological countermeasures [121]. This raises an important question: why not compare the capabilities of these recent technological advancements with established concepts in population genetics and theories that have been recognized over the years? Employing a new methodological and conceptual approach will undoubtedly benefit public health, practitioners, preventive services, and medical crisis teams.

4. Conclusions

Understanding genetic variation through the universal coefficients mentioned allows us to categorize bacteria alongside other haploid and diploid organisms. The selected H coefficients, which represent genetic diversity, enable us to estimate the genetic variation in bacteria. By comparing the values of these genetic diversity coefficients, we can test hypotheses regarding the evolutionary stage of the bacteria under examination. The specific value of the H parameter helps estimate the acquisition of DNA sequences within bacterial genomes, particularly concerning traits related to drug resistance in various pathogens. This understanding is crucial in light of the widespread pan-drug resistance observed in many bacterial species today.

With the wealth of knowledge regarding various bacterial genomes and the detailed DNA sequences now accessible, it is essential to reevaluate the current estimates of genetic diversity in bacteria. Traditional DNA scanning techniques designed for diploid organisms suggest that the genetic diversity among the analyzed bacterial individuals may be nearly three times greater than what has been indicated by enzymatic estimations [122]. Furthermore, the significant genetic diversity observed in bacterial genomes within known niches may suggest genome plasticity, hidden genetic potential, and a spreading population type. In contrast, stable values of diversity coefficients could reflect the intentional use of selected strains, resulting in a clonal population with a high rate of invasiveness. Additionally, a sudden emergence of unusually high genetic diversity in monitored bacterial populations might indicate their intentional application for harmful purposes or serve as an early sign of traits linked to public health and environmental hygiene concerns.

Understanding the genetic variability of bacterial populations in human communities, as well as the diversity of genes and proteins, is crucial. This is particularly relevant given the potential accidental or intentional use of genetically modified bacteria as unconventional weapons during hybrid warfare and crises. Identifying specific genetic markers and coefficients can serve as early warning signals for any unusual endemic or epidemic activity among bacteria within urban populations. This proactive approach can help prevent, protect, and preserve public health effectively. Monitoring the presence of bacteria in shed feces within sewer systems or outflows from wastewater treatment plants, while analyzing the genetic structure—whether panmictic or invasive—of bacterial populations and their level of clonality, is essential. The greater the genetic diversity of potential pathogens within the human population, the higher the actual threat of an epidemic (Figure 2).

According to Mata and Douardo [123], a matrix of values and numerical ranges can be created to assess the bacterial structure of populations and genetic diversity parameters across several categories. These include susceptible (S), exposed (E), asymptomatic infectious (A), symptomatic (I), pre-hospitalized in the ICU (PH), predeceased with a fatal prognosis (PD), admitted to the ICU with potential recovery (HR) or death (HD), recovered (R), and deceased (D) populations. Additionally, space can be included in the matrices for potential microbiological markers related to the intentional use of microorganisms that could threaten public health. During environmental systematic surveillance, values of “h” parameters will provide information or warnings about impending events and risks before they escalate into endemic or higher-level epidemic situations. The critical question revolves around the selection of genetic markers. Therefore, whole genome sequencing, along with randomly amplified regions and individual traits (such as enzymes and resistance genes), should be utilized simultaneously.